AAAI 2023technical1 citations
Incremental Density-Based Clustering with Grid Partitioning (Student Abstract)
Jeong-Hun Kim, Tserenpurev Chuluunsaikhan, Jong-Hyeok Choi, Aziz Nasridinov
Abstract
DBSCAN is widely used in various fields, but it requires computational costs similar to those of re-clustering from scratch to update clusters when new data is inserted. To solve this, we propose an incremental density-based clustering method that rapidly updates clusters by identifying in advance regions where cluster updates will occur. Also, through extensive experiments, we show that our method provides clustering results similar to those of DBSCAN.
BibTeX
@article{Kim_Chuluunsaikhan_Choi_Nasridinov_2024, title={Incremental Density-Based Clustering with Grid Partitioning (Student Abstract)}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26981}, DOI={10.1609/aaai.v37i13.26981}, abstractNote={DBSCAN is widely used in various fields, but it requires computational costs similar to those of re-clustering from scratch to update clusters when new data is inserted. To solve this, we propose an incremental density-based clustering method that rapidly updates clusters by identifying in advance regions where cluster updates will occur. Also, through extensive experiments, we show that our method provides clustering results similar to those of DBSCAN.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Kim, Jeong-Hun and Chuluunsaikhan, Tserenpurev and Choi, Jong-Hyeok and Nasridinov, Aziz}, year={2024}, month={Jul.}, pages={16242-16243} }